Python
Data analysis and scripting

Belagavi · Karnataka
Rated 4.8 stars on Google
Go from beginner to industry-ready with hands-on training in Python, SQL, data visualisation, machine learning and deep learning. Learn to work with real-world datasets, build and evaluate models, deploy them with Flask and Streamlit and complete practical projects using industry-relevant tools.
Ask about fees, batch timings and eligibility for Advanced Data Science and Artificial Intelligence. Prefer to talk now? Call +91 9108101795.
What You'll Learn
Get hands-on experience with Python, SQL, Scikit-learn, TensorFlow, PyTorch and 18+ tools used in modern data science and machine learning.
Data analysis and scripting

Data wrangling and computation
Data plotting and charting

Statistical data visualisation

Interactive charts
Database queries and management
Spreadsheets and pivot tables
Interactive dashboards and reports

Data visualisation and BI
Classical ML algorithms
Deep learning with Keras
Deep learning frameworks

Serving models through an API
Data apps for your models
Course Curriculum
Explore the complete week-by-week syllabus, from data analytics and Python to machine learning, deep learning and model deployment.
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Ensemble techniques and deployment basics
12 weeks · opens as you scroll
Phase 1 · Weeks 1–4Data analytics
Get to know what data science is, the kinds of data and sources you will work with, and the statistics and probability that sit behind it. Then learn how data is gathered, from APIs to the basics of scraping it from web pages.
Turn raw data into something you can trust by handling missing values and outliers and reshaping it for analysis. Then explore it, looking at one, two or many variables at a time and checking how they relate through correlation.
Show what the data says with charts and dashboards built in Matplotlib, Seaborn and Plotly. Then query it with SQL, from the basics to advanced work with joins, subqueries and aggregations.
Tools: Matplotlib, Seaborn, Plotly, SQL
Analyse data in Excel with formulas, pivot tables and its other analysis tools. Then tie the numbers to the business through KPIs and metrics and business case studies, closing the module with its toolkit of Excel, SQL, Python and Power BI or Tableau.
Tools: Excel, SQL, Python (Pandas, NumPy), Power BI / Tableau
Phase 2 · Weeks 5–8Machine learning
Learn what machine learning is and how its main types differ: supervised, unsupervised and reinforcement learning. Cover the maths behind it (linear algebra, calculus, probability and statistics), then prepare data for models with feature engineering, encoding, scaling and a train-test split.
Work through the core algorithms. For data with known answers, study linear and logistic regression, decision trees, random forest, support vector machines and K-nearest neighbours. For data without them, study K-means, hierarchical clustering, DBSCAN and PCA.
Learn to judge a model properly with accuracy, precision, recall, F1-score, the confusion matrix, ROC-AUC and cross validation. Then improve it with hyperparameter tuning by grid search and random search, and handle imbalanced data with over-sampling, under-sampling and SMOTE.
Combine models for better results with bagging, boosting and stacking. Then take a first step towards using a model in practice by saving it with Pickle or Joblib and serving it with Flask, and finish the module with the Python libraries it relies on.
Tools: Python, Scikit-learn, Pandas, NumPy, Matplotlib
Phase 3 · Weeks 9–12Deep learning
Meet deep learning: what it is and where it is used. Build up from the perceptron and activation functions to forward and backpropagation, then to deep neural networks with their architecture, hidden layers and training process.
See how convolutional neural networks read images through convolution, padding, stride, pooling layers and CNN architectures. Then move to recurrent networks for data that comes in sequence: RNN basics, LSTM, GRU and sequence prediction.
Apply deep learning to language with text preprocessing, word embeddings and an overview of transformers such as BERT and GPT. Learn how models are optimised through loss functions, optimisers like SGD and Adam, learning rate scheduling and regularisation, then put them to work in computer vision: image classification, object detection and transfer learning.
Build with the main deep learning frameworks, TensorFlow with Keras and PyTorch. Then deploy a model: save it, serve it through a Flask API and use Streamlit.
Tools: TensorFlow / Keras, PyTorch, Flask, Streamlit
Practical Learning
Explore the practical projects included in our curriculum, designed to help you apply Python, machine learning and deep learning skills to real-world problems.

Group customers by how they behave, using clustering methods such as K-Means, hierarchical clustering and DBSCAN, with PCA to see the groups.

Train a convolutional neural network to sort images into classes, then try transfer learning to build on a model that already exists.

Prepare text, turn its words into embeddings and build a classifier that sorts it into categories, with a first look at transformer models such as BERT.
The pictures illustrate each brief and are not student work.
Global Learning & Certifications
Access free courses, industry-recognised skill badges, professional certificates and real-world job simulations from 16 global learning platforms, complementing the technology programs offered at BIFT.

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FAQ
Quick answers about courses, who can join, certificates and placement support. For anything else, such as fees or batch timings, send us an enquiry.
On completion you can generate your Advanced Data Science and Artificial Intelligence certificate and become eligible for placement assistance if you meet both requirements:
Placement assistance is support with your job search: preparation, guidance and help reaching opportunities. It is not a guarantee of a job, an interview or a salary.
How placement assistance worksTalk to a learning advisor about the next batch, fees and timings, or read through the curriculum first.